Master Thesis in Emergent Width-Adaptive Representations via Spatially-Gated Channel Routing

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Interested in exploring new approaches in deep learning and computer vision? Join our research team and help shape the future of autonomous perception systems – apply now! You will develop and implement a novel and efficient transformer architecture for autonomous driving perception that adaptively adjusts its computational depth and width. As part of evaluating the approach, you will design and conduct experiments to assess model performance and efficiency for key perception tasks such as semantic segmentation and object detection, while comparing the results with several baseline models. Through detailed investigations, you will analyze emergent network properties, including the way computational resources are allocated to different image regions and how internal feature representations …

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